Shannon is an autonomous AI penetration testing system built to find and prove real, exploitable vulnerabilities in web applications rather than stopping at static warnings or best-guess alerts. It focuses on “proof by exploitation,” meaning it actively hunts for attack vectors in your code and then attempts to execute end-to-end exploits to demonstrate impact. The project blends source-aware analysis with automated web interaction so it can validate issues like injection flaws, authentication bypasses, and other exploitable paths in a way that resembles an actual attacker’s workflow. Instead of requiring you to manually reproduce findings, Shannon is designed to produce actionable evidence that a weakness can be weaponized, which helps teams prioritize what truly matters. It positions itself as a pre-attacker safety net, aiming to break your web app before someone else does and thereby reduce the gap between “potentially vulnerable” and “confirmed exploitable.”
Features
- Autonomous exploit-driven web app testing
- Source-aware attack vector discovery
- Built-in browser automation for real-world exploitation
- Focus on confirmed impact rather than speculative alerts
- Workflow designed to surface reproducible exploit evidence
- Benchmark-oriented evaluation mindset for measuring effectiveness